Variable definitions


The ticks_w_weather dataset contains the following variables:

  • site_id: The four-letter NEON site code
  • date: The first day of the MMWR week for this row
  • jd: The Julian date calculated from the date column
  • mmwr_year: The year of the MMWR week
  • mmwr_week: The week number of the MMWR week
  • mean_temp: The mean temperature of the current MMWR week. Calculated by taking the mean of daily mean temperatures
  • min_temp: The minimum temperature of the current MMWR week. Calculated by taking the minimum of daily minimum temperatures
  • max_temp: The maximum temperature of the current MMWR week. Calculated by taking the maximum of daily maximum temperatures
  • mean_rh: The mean relative humidity (%) of the current MMWR week. Calculated by taking the mean of daily mean RH values
  • rh_min: The minimum relative humidity (%) of the current MMWR week. Calculated by taking the minimum of daily minimum RH values
  • rh_max: The maximum relative humidity (%) of the current MMWR week. Calculated by taking the maximum of daily maximum RH values
  • mean_vpd: The mean vapor pressure deficit for the current MMWR week. Calculated by taking the mean of daily mean VPD values
  • mean_precip_mm: The mean daily precipitation (mm) for the current MMWR week. Calculated by taking the mean of daily precipitation sums
  • sum_precip_mm: Sum of daily precipitation (mm) for the current MMWR week. Calculated by summing the daily precipitation sums
  • dd: The average daily degree days accumulated over the current MMWR week
  • thirty_day_dd: The mean 30-day-rolling-sum of degree days for the current MMWR week. Calculated by taking the mean of daily thirty-day rolling sums
  • dd_30d_rollsum_lag34: The mean 30-day-rolling-sum of degree days for the MMWR week 34 weeks previous. To calculate: 1) Get thirty_day_dd calculations, 2) get the 34-week lagged values of those (i.e., lag(x = thirty_day_dd, n = 34L)), 3) average the values for all days in the current MMWR week
  • dd_30d_rollsum_lag42: Same as lag_thirty_day_dd_34wk, but 42 weeks previous
  • dd_30d_rollsum_lag50: Same as lag_thirty_day_dd_34wk, but 50 weeks previous
  • dd_30d_rollsum_prev_week: This is the thirty-day rolling sum of degree days from the last day of the previous MMWR week. No averaging takes place. e.g, if today is Sunday, then this is the 30-day rolling degree day sum of yesterday (Saturday)
  • cume_dd_prev_week: Similar to dd_30d_rollsum_prev_week, except this is the cumulative degree day count (starting Jan. 1) of the the last day of the previous MMWR week
  • cume_cd_prev_winter: The total number of “chill days” (see method below under Notes) from September 1st of the preceding year to March 31st of the current year. Values before April 1st use the previous year’s accumulations
  • amblyomma_americanum: The density of Amblyomma americanum ticks for the current MMWR week, reported as ticks per 1600m2
  • amam_filled: A version of the tick count column above that has been gap filled using linear interpolation
  • tick_interp_flag: A flag column that indicates whether the week’s value for amam_filled was observed or interpolated
  • amam_4wk_rollmean_lag1: The four-week rolling average of the interpolated tick count column, then lagged by one week
  • mean_vpd_4wk_rollmean_lag1: The four-week rolling average of the mean_vpd column, then lagged by one week
  • amam_4wk_rollmean_lag50: The four-week rolling average of the interpolated tick count column, then lagged by 50 weeks
  • mean_vpd_4wk_rollmean_lag50: The four-week rolling average of the mean_vpd column, then lagged by 50 weeks


Notes:

  • Degree days: mean(min_temp, max_temp) - 0. Then, if the results is positive, this is the number of degree days accumulated that day. Negative values (i.e., temps below 0C) do not count towards this.
  • Chill days = 0 - mean(min_temp, max_temp). Then, if the result is positive, this is the number of chill days accumulated that day. Negative values (i.e., temps above 0C) do not count towards this.


Dataset preview

site_id date jd iso_year iso_week iso_week_num mean_temp min_temp max_temp rh_min rh_max mean_vpd mean_precip_mm sum_precip_mm dd thirty_day_dd dd_30d_rollsum_lag34 dd_30d_rollsum_lag42 dd_30d_rollsum_lag50 dd_30d_rollsum_prev_week cume_dd_prev_week cume_cd_prev_winter amblyomma_americanum dd_rollsum_prev_week amam_filled tick_interp_flag amam_4wk_rollmean_lag1 mean_vpd_4wk_rollmean_lag1 amam_4wk_rollmean_lag50 mean_vpd_4wk_rollmean_lag50 amam_lag1 amam_lag2 amam_lag3 amam_lag4
BLAN 2015-04-20 110 2015 2015-W17 17 10.74286 1.25 26.55 25.6 100.0 0.7057143 5.3760000 37.632 10.74286 339.3714 106.7000 50.21429 28.74286 316.40 474.15 227.95 0.000000 108.00 0.000000 original 4.907976 0.8957143 4.907976 0.8957143 0.000000 0.000000 0.000000 0.000000
BLAN 2015-04-27 117 2015 NA 18 14.17143 4.15 26.75 23.9 96.9 0.8742857 2.5344286 17.741 14.17143 375.1643 140.0857 101.11429 45.05714 346.80 549.35 227.95 NA 75.20 3.271984 interpolated 4.907976 0.8957143 4.907976 0.8957143 0.000000 0.000000 0.000000 0.000000
BLAN 2015-05-04 124 2015 NA 19 20.85714 11.95 29.65 23.7 100.0 1.0357143 0.6911429 4.838 20.85714 423.9071 196.1286 135.16429 94.20714 386.50 648.55 227.95 NA 99.20 6.543967 interpolated 4.907976 0.8957143 4.907976 0.8957143 3.271984 0.000000 0.000000 0.000000
BLAN 2015-05-11 131 2015 2015-W20 20 19.85000 5.55 30.65 37.0 100.0 0.9671429 0.3840000 2.688 19.85000 475.8786 250.2643 185.31429 131.07857 451.20 794.55 227.95 9.815951 146.00 9.815951 original 4.907976 0.8957143 4.907976 0.8957143 6.543967 3.271984 0.000000 0.000000
BLAN 2015-05-18 138 2015 NA 21 17.50714 5.05 31.35 29.7 100.0 0.9071429 0.0000000 0.000 17.50714 506.6500 293.9000 245.22857 175.57857 492.20 933.50 227.95 NA 138.95 9.877301 interpolated 4.907976 0.8957143 4.907976 0.8957143 9.815951 6.543967 3.271984 0.000000
BLAN 2015-05-25 145 2015 NA 22 23.90000 16.05 31.45 37.2 96.3 1.1942857 2.5342857 17.740 23.90000 575.6571 343.8857 285.65000 239.02857 523.60 1056.05 227.95 NA 122.55 9.938650 interpolated 7.377301 0.9460714 4.907976 0.8957143 9.877301 9.815951 6.543967 3.271984
BLAN 2015-06-01 152 2015 2015-W23 23 18.05714 12.05 29.65 48.1 100.0 0.4171429 5.2225714 36.558 18.05714 603.8429 381.4357 339.37143 277.38571 607.80 1223.35 227.95 10.000000 167.30 10.000000 original 9.043967 1.0260714 4.907976 0.8957143 9.938650 9.877301 9.815951 6.543967
BLAN 2015-06-08 159 2015 2015-W24 24 25.27857 13.85 33.35 36.2 100.0 1.3342857 3.2252857 22.577 25.27857 615.4000 433.2786 375.16429 335.02857 598.30 1349.75 227.95 19.393939 126.40 19.393939 original 9.907975 0.8714286 4.907976 0.8957143 10.000000 9.938650 9.877301 9.815951
BLAN 2015-06-15 166 2015 NA 25 25.60714 19.55 32.55 45.7 92.5 1.1571429 2.4578571 17.205 25.60714 657.2429 480.5000 423.90714 369.49286 640.65 1526.70 227.95 NA 176.95 11.265597 interpolated 12.302473 0.9632143 4.907976 0.8957143 19.393939 10.000000 9.938650 9.877301
BLAN 2015-06-22 173 2015 2015-W26 26 22.68571 12.95 34.35 40.8 100.0 0.9171429 0.3840000 2.688 22.68571 698.8357 512.4857 475.87857 414.66429 686.15 1705.95 227.95 3.137255 179.25 3.137255 original 12.649547 1.0257143 4.907976 0.8957143 11.265597 19.393939 10.000000 9.938650



Quality control plots


Missing data:


Variables vs. data source:


Minimum temp

Timeseries

1:1


Maximum temp

Timeseries

1:1


Minimum RH

Timeseries

1:1


Maximum RH

Timeseries

1:1


VPD

Timeseries

1:1


Precip

Timeseries

1:1

Tick counts


Tick count line plot:


Raw tick counts overlaid on interpolation:


Chill days


Workflow diagram

tar_visnetwork()
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